Tuesday, January 22, 2013

2012 Election: Big Data Makes It Easy to Shoot Yourself In The Foot

A recent post in MIT Technology Review about the use of Big
Data by the parties in the 2012 election offers fascinating insights into what
works – and what doesn’t. Both sides had
operations specifically designed to glean new insights
about voters and apply them to helping their candidate win, and the article
interviews the director of each effort shortly after the election. As a result, we see long-term Big Data
projects whose results have just been tested against the reality of the voter
“market” and whose project reviews are relatively untainted by post-project
“spin.”

I should note that I view the article’s author’s grandiose
claims about the meaning of the Obama campaign’s relative success in Big Data
analytics to be overblown. Luckily, many
of those claims take up only the first 20% of the article. The rest provides a
fascinating insight into two approaches to Big Data, one of which demonstrably
worked better than the other.

Problem #1: Big Data as a “Hot Topic” vs. as a Strategy

The first contrast between the campaigns was that the Obama
one chose to bring the analytical systems in-house, whereas the Romney campaign
chose to use existing software and hardware run by someone else. In fact, the Obama campaign specifically
committed major bucks to a Vertica solution way back in 2009. This begs the question, however – why did the
two campaigns take such different approaches? After all, the Romney campaign
actually started quite early, around the same time, and had no real constraints
on money spent.

The answer, I think, is that while Obama viewed in-depth
analytics and trolling the Internet for more voter insights as a strategy,
while for the Romney campaign, the concept of Big Data was fuzzier, more of a
“hot topic” type of strategy. We have
all seen CEOs who have said, “Everyone’s talking about this new technology. We
must therefore do it.” The flavor of Romney-campaign
thinking, as reported in the article, was, “Big Data is clearly effective at
the corporations we look at; we must therefore get something that purports to
do Big Data and use it somehow to understand voters better.”

The result was a cautionary tale. The Romney system involved a third party
merging two sets of legacy-application data repeatedly. As a result, breakdowns occurred in the
stress of the late campaign, and analytics was unnecessarily delayed, delaying
candidate responses. The Obama campaign suffered from no such problems. It is
not clear how much the Obama campaign’s faster response time helped (although
it apparently helped change Obama’s style after the first debate). However, it is clear that the Romney
campaign’s fuzzy and delayed analytics helped lead to Problem #2.

Problem #2: Hearing the “Customer of
One” vs. Hearing What You Want To Hear

One of the great shocks of the campaign was that, in an
unprecedented way, the Romney campaign and the Republican party as a whole
deluded themselves about what was going to happen. And yet, the campaign’s Big Data analysis
mined a long tradition of understanding voter blocs, and could be supposedly
cross-checked against a broad array of pollsters – most of whom were themselves
deluded by their political biases.

As it
turned out, even the closest estimate underestimated Obama’s margin by about
0.5%. Moreover, on election day, the
Romney campaign actually thought they had a better than even chance of winning.
However, as it turned out, to win they would have had to amass more than 5.3%
more of the vote in a couple of battleground states than they did, at minimum.
To put it another way, they probably would have had to change the vote
percentages by more than 5% nationwide to win.
They weren’t even in the same ballpark.

By the way, for Nate Silver fans, he was off by about 1.5%.

There appeared to be two key insights that made this
election different. First, perhaps
one-third of people voting could only be reached for polling via cell phone –
and they voted in a significantly different way. Second, only perhaps 5% of the electorate
reached the Labor Day start of the campaign with a changeable vote – again, a
major difference from 20-40 years ago.
Gaffes, debates, Hurricane Sandy – it didn’t make that much difference.

Given this, the Romney campaign’s one faint hope was voter
turnout – unprecedented turnout for him in battleground states. And yet, there
was not the slightest appreciation of this in the campaign. Instead, the Romney campaign or related
groups spent enormous sums on TV ads in the obvious markets, to the point where
one station in Ohio was running more ads than programming. The crime wasn’t
that this was wasted money; it was that relatively little money was spent on
“get out our vote.”

In fact, most of the Romney campaign Big Data folks’
attention was on a series of seemingly baffling Obama ads in small markets
targeting small demographics. As a
result, by election day, the Big Data folks were suffering from the “fog of
war”, trying to counter something they did not understand.

Again, by contrast, the Obama campaign was focused on a new
insight garnered from their use of Big Data to identify the “customer of
one”. More specifically, they realized
that although most votes were unchangeable, smaller pockets of “conservative”
voters could actually change their mind if policies they valued and prioritized
were brought to their attention. These could be reached by, say,
social-program-highlighting ads aimed at conservative women in more rural Ohio
markets.

An under-appreciated part of these ads was that they contained
real content. After all, “conservative”
voters were being saturated with generic Romney ads. To succeed, the Obama ads needed to point to
real, verifiable programs.

The end of every political campaign is “get out the vote” –
maximize the number of ones supporters who actually vote on election day (and,
of course, the increasing percentage who vote before then). And this is where
the most startling difference between the campaigns’ approach to Big Data
emerges.

It is apparent from various reports that on election day,
the Romney campaign was prepared to focus its “turn out the vote” efforts on
key demographics in battleground states, fine-tuned by what their Big Data
analytics was telling them. And then,
right when voter turnout efforts were supposed to start, their feed from their
system went down. And it stayed down,
for most of the day.

But what almost passes belief is what the campaign did about
that. Local offices were begging to go
out and do “get out the vote” efforts anyway.
Instead, they were apparently told to wait until the system came up
again. Yes, theoretically, unfocused
efforts could have done more harm than good.
Practically, however, it was overwhelmingly likely that “feet on the
street” would have instead achieved slightly higher Romney turnout. And not
only national but local campaign coordinators failed to realize that.

The problem, it appears, was that no one had autonomy, and
all were focused on the Big Data part of the “get out the vote” strategy. By contrast, the Obama campaign – partly due
to an existing Internet-enabled strategy – granted local offices the ability to
act proactively, and the Big Data “customer of one” focus was only part of a
broader effort in battleground states to get out key Obama demographics, which
were already well understood pre-Big Data.

Again, it is worth noting that this made little difference
“on the day.” It is possible that it made a difference to several House seats;
but not enough to overcome the effects of the 2010 election and its resulting
Republican-dominated redistricting. Note
that there was apparently the biggest difference between the vote for each
party (Democrats +1%) and the House seat allocation (Republicans +4%) ever
recorded.

Implications for Organization Big-Data Use

We have seen, above, how the Romney organization used Big
Data to shoot themselves in the foot – and yet, their strategies were
superficially reasonable and well aligned with the practices in many
businesses. The immediate
recommendations for IT and the enterprise are likewise relatively
straightforward:

1.Treat Big
Data as a strategy, not as a “hot topic.”
Understand that for it to be successful, it must provide greater depth
and a more accurate view of the customer and of one’s own organization, and
those insights need to be translated to fine-tuning of strategies sooner rather
than later.

2.Focus on
Big Data’s ability to understand customers not only more deeply as global
“types” but in finer-grained groups, and ensure that the organization accepts a more realistic view of the
customer. Bluntly, the Romney
campaign started believing their own propaganda; where have we seen that
before? And one reason was that Big Data
was not telling them any different.

3.Adopt an
agile marketing strategy that does not hang on command-and-control top-down
implementation. Agile marketing has a great respect for in-depth data. However, it also has a great respect for the
way that data reflects or fails to reflect actual customers, and customers who
are constantly changing.

And that brings us to our final point. As I’ve noted, the success or failure of Big
Data efforts turned out to matter surprisingly little to the outcome of this
particular election. However, Republican
post-mortem efforts show that their failure to understand its implications has
drastically delayed – and we are talking more than a decade here – their
adaptation to changing American demographics.
They have created a party culture that makes it extremely difficult for
them to move to anything more than permanent minority status nationally,
because it carefully widened the divide with groups such as Hispanics in the
name of older white-male “get out the vote.”

The point I am trying to make is that if Big Data cements an
organization’s existing un-agile strategies in place, it is doing just as much
harm as good – even though, for now, things are going better than ever. The real value of Big Data, done well, is
that it not only enables you to understand your customer better, but it also
enables your organization to fit itself better to the customer – bottom-up as
much as top-down – and both of them to understand how the customer is changing,
not just what the customer is like now.

So what is it going to be?
Are you going to use Big Data to shoot yourselves in the foot, or to
deliver better strategies, better implemented?
Inquiring Republicans want to know.

Wayne Kernochan

About Me

I have recently retired. Before retirement, I was a long-time computer industry analyst at firms like Aberdeen Group and Yankee Group, and before that a programmer at Prime Computer and Computer Corp. of America. Sloan/MIT MBA, Cornell Computer Science Master's, and Harvard college degrees. Used to play the violin, and have written unpublished books about personal finance, violin playing, and the relationship between religion and mathematics, as well as three plays, two musicals, a screenplay on climate change, short stories, and poetry. I intend to use this blog in future both to continue to enjoy the computing field and to pursue my interests in many other areas (e.g., climate change, history, issues of the day).